Geostationary satellites collect high-resolution weather data comprising a series of images which can be used to estimate wind speed and direction at different altitudes. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in images taken by the GOES-R series of the NOAA geostationary meteorological satellites. However, the wind estimates from the DMW Algorithm are sparse and do not come with uncertainty measures. This motivates us to statistically model wind motions as a spatial process drifting in time. We propose a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction. We estimate the ...
The Center for Weather Forecast and Climatic Studies (CPTEC/INPE) developed a set of Cloud Drift Win...
The NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High-Resolution Radi...
Global wind observations are fundamental for studying weather and climate dynamics and for operation...
Geostationary satellites collect high-resolution weather data comprising a series of images which ca...
The objective of this study is to improve the characterization of satellite-derived atmospheric moti...
Current NESS winds operations provide approximately 1800 high quality wind estimates per day to abou...
Estimation of the atmospheric wind field based on cloud tracking using a time sequence of satellite ...
This study introduces a validation technique for quantitative comparison of algorithms which retriev...
Motions deduced in animated water vapor imagery from geostationary satellites can be used to infer w...
Signatures of directional wind waves are discovered after deconvolution of delay–Doppler maps in Glo...
This study will involve two objectives: (1) to develop, through computer simulations, optimal satell...
International audienceAt small time scales, the spatio-temporal variability of downwelling surface s...
Atmospheric motion vectors (AMVs), derived from the current GOES series of satellites, provide inval...
Techniques to integrate meteorological data from various satellite sensors to yield a global measure...
In real-time guidance strategies, the characteristics of the wind and wind modeling plays an importa...
The Center for Weather Forecast and Climatic Studies (CPTEC/INPE) developed a set of Cloud Drift Win...
The NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High-Resolution Radi...
Global wind observations are fundamental for studying weather and climate dynamics and for operation...
Geostationary satellites collect high-resolution weather data comprising a series of images which ca...
The objective of this study is to improve the characterization of satellite-derived atmospheric moti...
Current NESS winds operations provide approximately 1800 high quality wind estimates per day to abou...
Estimation of the atmospheric wind field based on cloud tracking using a time sequence of satellite ...
This study introduces a validation technique for quantitative comparison of algorithms which retriev...
Motions deduced in animated water vapor imagery from geostationary satellites can be used to infer w...
Signatures of directional wind waves are discovered after deconvolution of delay–Doppler maps in Glo...
This study will involve two objectives: (1) to develop, through computer simulations, optimal satell...
International audienceAt small time scales, the spatio-temporal variability of downwelling surface s...
Atmospheric motion vectors (AMVs), derived from the current GOES series of satellites, provide inval...
Techniques to integrate meteorological data from various satellite sensors to yield a global measure...
In real-time guidance strategies, the characteristics of the wind and wind modeling plays an importa...
The Center for Weather Forecast and Climatic Studies (CPTEC/INPE) developed a set of Cloud Drift Win...
The NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High-Resolution Radi...
Global wind observations are fundamental for studying weather and climate dynamics and for operation...